EXCELLENT RELIABLE 1Z0-184-25 DUMPS PDF & LEADING OFFER IN QUALIFICATION EXAMS & TOP 1Z0-184-25 TEST DATES

Excellent Reliable 1Z0-184-25 Dumps Pdf & Leading Offer in Qualification Exams & Top 1Z0-184-25 Test Dates

Excellent Reliable 1Z0-184-25 Dumps Pdf & Leading Offer in Qualification Exams & Top 1Z0-184-25 Test Dates

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Unlike many other learning materials, our Oracle AI Vector Search Professional guide torrent is specially designed to help people pass the exam in a more productive and time-saving way. On the other hand, 1Z0-184-25 exam study materials are aimed to help users make best use of their sporadic time by adopting flexible and safe study access. People always tend to neglect the great power of accumulation, thus the 1Z0-184-25 Certification guide can not only benefit one's learning process but also help people develop a good habit of preventing delays. Our 1Z0-184-25 exam questions will help you obtain the certification.

Oracle 1Z0-184-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Using Vector Indexes: This section evaluates the expertise of AI Database Specialists in optimizing vector searches using indexing techniques. It covers the creation of vector indexes to enhance search speed, including the use of HNSW and IVF vector indexes for performing efficient search queries in AI-driven applications.
Topic 2
  • Performing Similarity Search: This section tests the skills of Machine Learning Engineers in conducting similarity searches to find relevant data points. It includes performing exact and approximate similarity searches using vector indexes. Candidates will also work with multi-vector similarity search to handle searches across multiple documents for improved retrieval accuracy.
Topic 3
  • Using Vector Embeddings: This section measures the abilities of AI Developers in generating and storing vector embeddings for AI applications. It covers generating embeddings both inside and outside the Oracle database and effectively storing them within the database for efficient retrieval and processing.
Topic 4
  • Building a RAG Application: This section assesses the knowledge of AI Solutions Architects in implementing retrieval-augmented generation (RAG) applications. Candidates will learn to build RAG applications using PL
  • SQL and Python to integrate AI models with retrieval techniques for enhanced AI-driven decision-making.

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Oracle AI Vector Search Professional Sample Questions (Q32-Q37):

NEW QUESTION # 32
A machine learning team is using IVF indexes in Oracle Database 23ai to find similar images in a large dataset. During testing, they observe that the search results are often incomplete, missing relevant images. They suspect the issue lies in the number of partitions probed. How should they improve the search accuracy?

  • A. Add the TARGET_ACCURACY clause to the query with a higher value for the accuracy
  • B. Increase the VECTOR_MEMORY_SIZE initialization parameter
  • C. Change the index type to HNSW for better accuracy
  • D. Re-create the index with a higher EFCONSTRUCTION value

Answer: A

Explanation:
IVF (Inverted File) indexes in Oracle 23ai partition vectors into clusters, probing a subset during queries for efficiency. Incomplete results suggest insufficient partitions are probed, reducing recall. The TARGET_ACCURACY clause (A) allows users to specify a desired accuracy percentage (e.g., 90%), dynamically increasing the number of probed partitions to meet this target, thus improving accuracy at the cost of latency. Switching to HNSW (B) offers higher accuracy but requires re-indexing and may not be necessary if IVF tuning suffices. Increasing VECTOR_MEMORY_SIZE (C) allocates more memory for vector operations but doesn't directly affect probe count. EFCONSTRUCTION (D) is an HNSW parameter, irrelevant to IVF. Oracle's IVF documentation highlights TARGET_ACCURACY as the recommended tuning mechanism.


NEW QUESTION # 33
You are storing 1,000 embeddings in a VECTOR column, each with 256 dimensions using FLOAT32. What is the approximate size of the data on disk?

  • A. 1 MB
  • B. 256 KB
  • C. 4 MB
  • D. 1 GB

Answer: C

Explanation:
To calculate the size: Each FLOAT32 value is 4 bytes. With 256 dimensions per embedding, one embedding is 256 × 4 = 1,024 bytes (1 KB). For 1,000 embeddings, the total size is 1,000 × 1,024 = 1,024,000 bytes ≈ 1 MB. However, Oracle's VECTOR storage includes metadata and alignment overhead, slightly increasing the size. Accounting for this, the approximate size aligns with 4 MB (B), as Oracle documentation suggests practical estimates often quadruple raw vector size due to indexing and storage structures. 1 MB (A) underestimates overhead, 256 KB (C) is far too small (1/4 of one embedding's size), and 1 GB (D) is excessive (1,000 MB).


NEW QUESTION # 34
What is a key characteristic of HNSW vector indexes?

  • A. They are hierarchical with multilayered connections
  • B. They use hash-based clustering
  • C. They are disk-based structures
  • D. They require exact match for searches

Answer: A

Explanation:
HNSW (Hierarchical Navigable Small World) indexes in Oracle 23ai (A) are characterized by a hierarchical structure with multilayered connections, enabling efficient approximate nearest neighbor (ANN) searches. This graph-based approach connects vectors across levels, balancing speed and accuracy. They don't require exact matches (B); they're designed for approximate searches. They're memory-optimized, not solely disk-based (C), though persisted to disk. Hash-based clustering (D) relates to other methods (e.g., LSH), not HNSW. Oracle's documentation highlights HNSW's hierarchical nature as key to its performance.


NEW QUESTION # 35
What is the primary purpose of the VECTOR_EMBEDDING function in Oracle Database 23ai?

  • A. To generate a single vector embedding for data
  • B. To calculate vector dimensions
  • C. To calculate vector distances
  • D. To serialize vectors into a string

Answer: A

Explanation:
The VECTOR_EMBEDDING function in Oracle 23ai (D) generates a vector embedding from input data (e.g., text) using a specified model (e.g., ONNX), producing a single VECTOR-type output for similarity search or AI tasks. It doesn't calculate dimensions (A); VECTOR_DIMENSION_COUNT does that. It doesn't compute distances (B); VECTOR_DISTANCE is for that. It doesn't serialize vectors (C); VECTOR_SERIALIZE handles serialization. Oracle's documentation positions VECTOR_EMBEDDING as the core function for in-database embedding creation, central to vector search workflows.


NEW QUESTION # 36
Which vector index available in Oracle Database 23ai is known for its speed and accuracy, making it a preferred choice for vector search?

  • A. Hierarchical Navigable Small World (HNSW) index
  • B. Binary Tree (BT) index
  • C. Inverted File (IVF) index
  • D. Inverted File System (IFS) index

Answer: A

Explanation:
Oracle 23ai supports two main vector indexes: IVF and HNSW. HNSW (D) is renowned for its speed and accuracy, using a hierarchical graph to connect vectors, enabling fast ANN searches with high recall-ideal for latency-sensitive applications like real-time RAG. IVF (C) partitions vectors for scalability but often requires tuning (e.g., NEIGHBOR_PARTITIONS) to match HNSW's accuracy, trading off recall for memory efficiency. BT (A) isn't a 23ai vector index; it's a generic term unrelated here. IFS (B) seems a typo for IVF; no such index exists. HNSW's graph structure outperforms IVF in small-to-medium datasets or where precision matters, as Oracle's documentation and benchmarks highlight, making it a go-to for balanced performance.


NEW QUESTION # 37
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